• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.211-219
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• 2008

Customer Loyalty is very important for a company to be survived and to make profit for a long time. Especially, since the credit card company has to manage proper card members and merchants, the CRM(Customer Relationship Management) is much emphasized. A loyalty score is more essential to credit card companies which provide differential financial services based on card members and merchants than any other companies. In this paper, we discuss behavioral measures to define customer loyalty and suggest a method to make loyalty score with an example of a credit card company. The loyalty score developed is considered easy to understand and simple to apply in card industry. In the development of loyalty score, first, we define the loyal customers and non-loyal customers by measuring variables indicating loyalty. And we perform individual logistic regression by each exploratory measuring variable and obtain the weight of measure variables using Chi-square statistics which is used for model fitness. The loyalty score suggested shows very stable results in terms of PSI (Population Stability Index) as time goes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.5-15
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• 2015

Electronic transactions have been increasing with the development of the high-speed Internet and wireless communication. However, in recent years financial corporations and mobile carriers were attacked by hackers. And large numbers of privacy information have been leaked. In particular, in the case of credit card information can be misused in the online transaction, and the damage of this given to cardholder. To prevent these problems, it has been proposed to use a virtual card number instead of the actual card number. But it has security vulnerability and requires additional security infrastructure. In this paper, we analyzed the proposed virtual card number schemes. and we propose a new virtual credit card number scheme. In the newly proposed scheme, cardholder generates a key pair (public key/private key) and pre-register public key to the issuer. then, cardholder can pay no additional security infrastructure while still efficiently satisfy the security requirements.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.4
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• pp.161-176
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• 2013

This study discusses some features observed in Korean credit card industry, which is classified as a two-sided market. First, I summarize six characteristics or conditions that two-sided markets possess by reviewing related literatures, and consider if we can find out those peculiarities in Korean card industry. Second, this study casts light upon the unique issues which have not been handled in previous studies, that is another two-sidedness in mature two-sided markets. In the context of credit card industry, this additional two-sidedness is created by specific consumers group existing in entire consumer group in a card firm as a form of subset and specific partnering merchants group offering special benefits to their subset consumer group, or alliance card holders. This concept gives new idea that there could be multiple two-sidedness especially in mature two-sided markets, which will be significant to platforms' pricing strategies and expand the scope of related studies.

• Information Systems Review
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• v.8 no.3
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• pp.65-80
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• 2006

This paper proposes a new infrastructure that supports divisible card payment where a combination of multiple credit cards can be used for a single purchase. The divisible card payment infrastructure modifies the existing payment system in two ways. First, the V-Card Manager(VCM) is added to the merchant side to handle the divisible card approval process from respective credit-card issuers. Second, the V-Card Agent(VA) is added to the customer side and generates a customized divisible card, called V-Card, based on the customer's preferences. This paper provides a customizing card payment method that supports divisible payments based on profits and preferences of customers.

• International Commerce and Information Review
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• v.15 no.2
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• pp.189-206
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• 2013

In this paper, we investigate if the new merchant fee system has problems. The fee was fixed by government policies and there were no processes of listening to market participants. Therefore, we suggest improvements as follows. First, large-sized stores take burdens for the rest of stores so credit card companies and the government should try to minimize the increasing rate for large-sized stores. Second, credit card firms need to give VAN companies roles of stabilizing authorizing processes. Third, periodic update requires because of credibility. Fourth, contents of merchant fee should be disclosed to merchants. Fifth, there needs discussions for merchant fees of debit cards.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.824-829
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• 2009

SMS service is widely used for providing the approval information of a credit card when the payment is made by a credit card. But the message contained in the SMS is very simple and includes one time approval information. Although detailed approval informations could be retrieved by means of wire-network or mobile network services, the wire-network service could not be used on moving and the mobile network service requires high cost. In this paper, a WIPI application program managing the approval informations of a credit card based on the accumulated SMS messages in the cell phone has been developed. A cell phone user can conveniently can get the useful information such as the approval list on last month, the approval list on this month, the total amount on this month and the information about payment by installment from the developed program without any cost.

• Journal of the Korean Home Economics Association
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• v.45 no.2
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• pp.63-75
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• 2007

The purpose sof this study was to examine consumer's addictive buying through the TV home shopping, focused on materialism, show host and credit card use consumer. Two hundred four female consumers who had purchased fashion items in TV home shopping had participated for this study. For data analysis, descriptive statistics, factor analysis, t-test, and multiple regression were used for the study with SPSS 12.0 program. The result were as follows. First, the addictive buying group was 13.2% among respondents while non-addictive buying group was 86.8%. Second, materialism was classified into 3 factors: happiness, success and possessiveness. Show host was classified into 3 factors such as visual presentation, image and communication skill. Credit card was classified into 3 factors: sales promotion, late payment and method payment. Results of multiple regression revealed that happiness and possessiveness from materialism, credit card late payment, and show host communication skill were significantly related to addictive buying. Based on these results, this study would provide fashion marketing strategies for reasonable and reasonable consuming culture, since addictive buying has been not only individual problem but also social problem. Based on these results, this study would provide fashion marketing strategies for reasonable and reasonable consuming culture, since addictive buying has been not only individual problem but also social problem.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.2 no.2
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• pp.121-127
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• 2001

The purpose of this paper is to verify and statistically analyze the effect of credit card expenditure on money demand. Statistical analysis was Performed regarding credit spending volume. Ml, M2, M3, non-monetary banking deposit (OFI) and many other parameters based on time-series data for 14 years, from 1985 to 1998. The results suggest that credit card is not the main cause of inflation or increase of money supply but it would become an economic creation on contributing to human life in the coming century. Therefore the monetary authorities must develop the credit card industry, so as to improve positive function of credit cards and to keep controlling some of its negative functions minimally.